Partial Least Squares Based Gene Expression Analysis in EBV- Positive and EBV-Negative Posttransplant Lymphoproliferative Disorders

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

Background

Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.

Results

We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.

Conclusions

Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users.

Pathway and network analysis in glioma with the partial least squares method

Gene expression profiling facilitates the understanding of biological characteristics of gliomas. Previous studies mainly used regression/variance analysis without considering various background biological and environmental factors. The aim of this study was to investigate gene expression differences between grade III and IV gliomas through partial least squares (PLS) based analysis. The expression data set was from the Gene Expression Omnibus database. PLS based analysis was performed with the R statistical software. A total of 1,378 differentially expressed genes were identified. Survival analysis identified four pathways, including Prion diseases, colorectal cancer, CAMs, and PI3K-Akt signaling, which may be related with the prognosis of the patients. Network analysis identified two hub genes, ELAVL1 and FN1, which have been reported to be related with glioma previously. Our results provide new understanding of glioma pathogenesis and prognosis with the hope to offer theoretical support for future therapeutic studies.